Enterprise Guide to AI Readiness

If you're responsible for AI transformation at an enterprise with 1,000 or more employees, you're dealing with a different problem than most organizations face.

You're not deciding whether to invest in AI readiness. You're figuring out how to make it work across thousands of people, competing priorities in multiple divisions, distributed teams, and business units moving at different speeds. What works in a 200-person company may not work at 2,000.

This guide is for C-suite executives, CHROs, transformation leaders, and business unit heads who need to understand what's different at enterprise scale and what infrastructure can handle that complexity.

What you'll learn

1. The infrastructure gap that can prevent enterprise AI investments from translating into results.
2. The C-suite visibility problem that can cost momentum, trust, and talent.
3. What LinkedIn's experience illustrates about developing leaders during enterprise AI adoption.
4. ROI frameworks that connect readiness investments to business velocity.

For deeper context on AI readiness fundamentals, read Why AI Readiness Starts with People, Not Tech.

Two historical research findings illustrate the scale of expectations for AI: PwC's estimate that AI could contribute up to $15.7 trillion to the global economy by 2030, and a KPMG CEO Outlook finding that 67% of CEOs expected ROI within one to three years. These are projections and survey expectations, not realized returns.

Having technology, budget, and talent does not by itself create the leadership infrastructure needed to operate across complex enterprise operations. Employees may already be using AI more extensively than executives realize. The challenge is to connect that activity with coordinated execution.

The Torch Leadership Evolution Survey 2025, covering 176 enterprise organizations, found that middle management and project leads carried much of the transformation work. The selected categories below are not a complete distribution of every response category.

Source: Torch Leadership Evolution Survey 2025.

The disconnect compounds at scale

The people responsible for making AI work day to day face different challenges than the people setting strategy. For middle managers, the strategic opportunity can arrive as daily operational disruption that they must help their teams navigate.

When an experiment fails, executives might see a learning opportunity. The frontline manager sees a team that's now afraid to try anything new. When leadership announces embracing change, managers hear their team members asking if they should start looking for other jobs.

A visibility gap emerged in McKinsey's 2025 Superagency in the Workplace research: executives estimated that 4% of employees used AI for significant work, compared with a reported employee figure of 13%. Leaders may not see what is happening on the ground. Employees do not necessarily wait for official channels when those channels move too slowly.

Teams run experiments. Some show promise. Others stall between pilot and scaled implementation. Moving from something that worked in one team to something that works across divisions requires organizational infrastructure as well as technology.

Infrastructure, not individuals

The enterprise execution gap isn't simply about individual leaders lacking capability. It's about building the systems that help leaders develop what transformation requires at scale.

Small companies can transform through proximity and direct communication. At enterprise scale, you're dealing with fundamentally different challenges.

Federated decision-making

Every division operates with autonomy: different priorities, different budgets, different risk tolerances. Without coordination infrastructure to align AI strategy across business units, pilots can multiply while integration stalls.

Distributed teams across geographies

Global operations span time zones, cultures, and regulatory environments. Headquarters-centric programs don't automatically translate. You need consistent capability development that adapts to local context.

Siloed adoption

Marketing builds AI tools without coordinating with sales. Engineering doesn't involve customer success. Innovation happens in pockets but doesn't scale horizontally. Each function solves the same problems independently.

Legacy systems and entrenched processes

Unlike startups building on clean foundations, you're navigating decades of technical debt and established workflows. You can't move fast and break things when breaking things affects thousands of customers.

Change fatigue

Teams are exhausted by constant transformation initiatives. Each new mandate feels like more pressure without more support. Cynicism builds. This too shall pass becomes the default mindset.

Multiple concurrent transformations

AI adoption happens alongside cloud migration, organizational restructuring, and market expansion. Each creates its own demands on leadership. They can compound instead of complementing one another.

Trust erodes faster at enterprise scale

In the Torch Leadership Evolution Survey 2025, 52% of enterprise leaders named building trust as their organization scales as their top challenge. Trust matters as AI reshapes how decisions get made and work gets distributed. But trust gets harder to build when you're moving fast and everything feels uncertain.

The survey found trust-related challenges rising with company size. The behaviors that build trust in a 200-person company don't automatically translate to 2,000 people across multiple geographies and business units.

Source: Torch Leadership Evolution Survey 2025.

How prepared do enterprise leaders feel?

In the same 2025 survey, 37% of enterprise leaders felt prepared for AI-driven change, leaving 63% who did not feel ready. Competing priorities were the most common barrier to getting the development they needed, cited by 32% as their biggest challenge. These findings describe a context in which everything feels urgent and the pace keeps accelerating.

These enterprise-specific challenges require purpose-built infrastructure rather than assuming that approaches designed for smaller organizations will transfer unchanged. The complexity isn't just bigger numbers. It involves different problems.

Readiness is the bridge between innovation and impact.

Companies that build readiness infrastructure can establish advantages that are difficult to replicate. Leaders develop capabilities that work across situations. Teams get comfortable with uncertainty. The aim is for each transformation to strengthen the system for the next.

Without that support, each initiative can get harder. People get tired of change that doesn't stick. They stop believing leadership knows what it's doing.

What failure looks like at enterprise scale

The guide describes an unnamed enterprise technology company that mandated all teams become AI-first by year-end. It built a readiness rubric, launched hackathons, created a champion network, and rolled out technical training. Six months in, the account describes lots of activity but minimal transformation. The PDF does not identify the company or provide independent outcome data for this example.

The gap

Every initiative required managers to navigate fear, create safety for experimentation, and debrief failures productively. None of the initiatives taught them how. When experiments failed, managers couldn't extract learning. Teams learned to avoid risk. Fear cascaded instead of curiosity.

The lesson is to avoid concluding that people were not ready when leadership had not been equipped to guide the transformation. The distinction matters: a lack of support for leading change is not the same as a lack of employee willingness or capability.

The talent loss multiplier

DDI's Global Leadership Forecast 2025 reported that 40% of stressed leaders were considering leaving their roles. This is a historical survey finding, not a prediction that 40% will leave.

At enterprise scale, talent loss has effects beyond the vacancy. When a senior leader with 10 years of institutional knowledge leaves, you lose more than expertise. You lose relationships, context, and the ability to move fast on decisions that require organizational memory.

The leaders who know how to navigate complexity and build trust under pressure have options. Continuing to ask them to do more without better support creates risks for the current AI transformation and for future change initiatives.

How the gap compounds

Teams remember pilots that stalled. Managers recall experiments that failed without productive debrief. Cynicism spreads through informal networks across divisions. Each failed transformation can make the next one harder.

Building readiness infrastructure is intended to help organizations move faster, integrate better, and keep their best people. Those advantages can compound over time and become harder for others to catch up to.

The original guide describes LinkedIn rolling AI tools out across its workforce and identifying a need to help leaders guide teams through the shift. Its account connects Torch's 360 assessments with development needs in change management and adaptability.

The case discussion brings together three elements: coaching for individuals and teams, AI practice to reinforce development, and organizational intelligence to help identify where teams need support. The central lesson is to connect leader development with a specific organizational change, rather than treating coaching as a separate activity.

Coaching was directed toward the capacities leaders needed during the transition. Practice supported development between sessions. Aggregated organizational patterns were intended to help executives understand barriers to adoption, where trust was building, and where concerns needed attention.

It's been such an amazing journey. We've worked with thousands of people over the last year, bringing a more precise way of coaching into LinkedIn. We're developing them toward a goal and toward an outcome that the company really needs from them.

At enterprise scale, there are two connected aims: developing leaders and providing organizational intelligence. Leaders work on navigating complexity and building trust under pressure. Executives gain insight into patterns across divisions and geographies. Development and organizational learning can happen continuously rather than only in annual cycles.

The intelligence loop depends on distinguishing confidential individual coaching from aggregated organizational themes. Those themes can help show where an organization needs attention, where resistance may be building, and what is working well enough to consider across other business units. Aggregated reporting is not the same as sharing individual coaching conversations.

Teams can rehearse difficult conversations before they happen. Managers can learn to debrief failed experiments productively. Leaders can build psychological safety during uncertainty. Together, these practices are intended to reduce friction, make experiments more useful, and create conditions for innovation.

Alongside LinkedIn, the original guide names Reddit, Tripadvisor, Twitch, FICO, and CrossCountry Consulting as organizations using Torch for leadership development. These references describe the guide's publication context; they do not establish that each customer used every component or achieved the same result.

The broader enterprise proposition is to develop leadership capacities that transfer across situations while helping executives understand organizational patterns across people, teams, and divisions.

Torch's Change Agent approach is intended to make transformation continuous across an enterprise. Three integrated components work together rather than functioning as separate initiatives.

Experienced coaches build lasting capacities

Coaches with leadership and transformation experience understand pressure, politics, and tradeoffs at scale. Coaching connects personal growth to what the organization needs.

A healthcare leader implementing AI diagnostics across multiple hospitals faces different trust issues than a financial services leader deploying fraud detection globally. Development needs to connect to real work, real teams, and transformation challenges specific to the business.

Always-on support reinforces new behaviors

Leaders can rehearse difficult conversations before they happen: explaining role changes, addressing fears about replacement, debriefing failed experiments, and navigating conflict when teams disagree about AI direction. Spark AI Agent supports practice between coaching sessions.

When the pressure moment comes, the aim is for the response to feel practiced rather than forced. Practice can be revisited as situations and organizational context change.

Visibility across business units

Organizational intelligence is intended to help executives see patterns in where transformation is building momentum and where it is stalling across divisions and geographies. This can complement, rather than rely exclusively on, periodic surveys.

Aggregated themes can surface concerns, show where support may be needed, and identify what is working elsewhere in the organization. This connects organizational reporting with confidential coaching, keeping organizational themes distinct from individual coaching conversations.

A staged rollout begins with a pilot in one function, expansion across divisions, then broader availability. A progression of 20, 200, and 2,000 leaders is a planning illustration, not a current program-size commitment or guarantee. The relevant principle is to account for different business contexts and regulatory environments as access expands.

Three components, one continuous system

When coaching, practice, and organizational intelligence are integrated, the aim is to sustain change instead of treating development as a program that simply ends.

The purpose of building AI readiness is to make each wave of change strengthen the organization's capacity for the next one.

ROI from readiness investments shows up differently than the delivery of technology features. The question is whether organizational capability is improving the ability to execute change across people, functions, and divisions.

The following dimensions are a measurement framework, not a promise of results. The numerical changes used in the original guide are illustrative scenarios, not documented customer outcomes or industry benchmarks.

Change velocity across divisions

Track time from a strategy decision to execution impact. The original guide illustrates a reduction from 18 months to 6 months. That is an example of the kind of change one might measure, not an observed result reported with supporting data. The underlying question is whether leaders can navigate ambiguity and teams can trust the direction without cutting corners.

Adoption depth and pilot-to-production conversion

Track whether AI tools move from pilot to production and whether integration reaches across functions. The guide illustrates a change from 5% to 50% across divisions. Treat those numbers only as a hypothetical comparison, not as a typical production rate or an expected return from Torch. Establish the actual baseline and outcome for your own program.

Cross-functional coordination improvements

Look for evidence that silos are breaking down: marketing coordinates with sales, engineering involves customer success, and shared learning mechanisms emerge. The aim is to stop relearning the same things in every business unit.

Leadership confidence and retention at scale

Examine whether trust between organizational levels is improving, frontline leaders feel supported, and executives have a clearer view across geographies. Retention is another dimension: are leaders staying engaged during change, and is the organization retaining institutional knowledge? These are outcomes to assess, not automatic consequences of a program.

Continuous change capacities

Consider whether a successful transformation builds capacity for the next across the enterprise. Leaders who have navigated one shift may be better prepared for the following one. The framework asks whether the organization is developing institutional capability, not merely completing a single initiative.

Measuring what matters for enterprise change

In board meetings, move beyond explaining why AI pilots stalled across divisions. Show which initiatives scaled and why. Rather than defending training budgets through activity alone, connect leadership development with business velocity.

Instead of relying solely on generic engagement scores, examine which readiness investments contributed to transformation across business units. A continuous feedback loop connects the development work, what is happening in the organization, and the next decision about support.

The reactive enterprise

Pilots multiply across divisions but stall before reaching production. Executives set strategy while business units work around it. Development happens in disconnected silos. By the time quarterly surveys reveal problems, damage may already be done.

Change initiatives pile up, creating exhaustion rather than momentum. Leaders spend their time putting out fires. Trust erodes with every failed initiative. This creates a loop that drains momentum and competitive advantage:

• Pilots stall before reaching scale.
• Strategy and execution drift apart.
• Leaders burn out without support systems.
• Quarterly surveys catch problems too late.
• Each failed initiative breeds more cynicism.

Staying reactive can cost momentum, trust, and talent. But there is a way out.

Ready enterprises break the loop

Ready enterprises approach change differently. Experiments become capabilities because leaders know how to navigate the messy middle of transformation. Strategy and execution stay aligned through continuous feedback across divisions. Development connects to what teams actually need.

The aim is to spot resistance early enough to address it and see what is working fast enough to scale it. Each transformation builds capability for the next. Leaders guide change with confidence instead of only reacting to problems. Trust grows stronger.

The ready enterprise replaces that reactive pattern with:

• Experiments scale into enterprise capabilities.
• Strategy and execution stay aligned.
• Leaders develop through transformation, not despite it.
• Timely organizational intelligence enables proactive support.
• Each change builds capacity for the next.

The difference is not a destination. It is infrastructure intended to make each transformation easier than the last across the enterprise. Building it creates organizational capabilities that others cannot simply copy overnight.

The ready enterprise is not a finish line. It is a commitment to the practices that make change an advantage.

AI transformation at enterprise scale is still developing. Build change infrastructure alongside technology so that pilots can become lasting organizational capabilities. The focus extends across people, divisions, and distributed teams.

When evaluating support, look beyond a catalog of coaching sessions, access to an AI practice tool, a set of consulting recommendations, or a completed training course in isolation. Ask how those activities connect to business goals, build internal capability, and help leaders apply what they know in complex situations. Practice, experienced judgment, and organizational learning have different roles to play.

Built for enterprise transformation

Torch connects change coaching, practice, and organizational intelligence. The aim is to keep transformation moving by developing leaders and revealing patterns that help or hinder change, so leaders and organizations can evolve together rather than in isolation.

Coaches who have led through change

Experienced coaches can bring empathy, foresight, and practical tools to the pressure of transformation. The emphasis is on helping leaders grow through challenge, not only talk about it.

Support for pressure moments

Spark AI Agent lets leaders rehearse high-stakes conversations before they happen. The goal is to build clarity and confidence through practice so that leaders are better prepared when the moment arrives.

Intelligence that ties growth to transformation goals

Organizational intelligence connects leader development with business priorities through aggregated themes and trends. It can help organizations identify barriers and improve their systems alongside their leaders while maintaining a distinction between organizational reporting and confidential individual coaching.

Evolving alongside customers

The original guide states that Torch uses its Change Agent approach internally as well as with clients. It describes that shared commitment as a way to stay grounded in what works.

The guide names LinkedIn, Reddit, Tripadvisor, Twitch, FICO, and CrossCountry Consulting in its discussion of leadership development during transformation. These historical customer references are not a claim that each organization used every component of the approach or experienced identical outcomes.

Start by assessing where your organization is on AI readiness. Identify the specific leadership gaps that could stall transformation, and map what closing those gaps could look like at your scale.

The purpose is to gain clarity on your readiness state and what to prioritize, whether or not you ultimately work with Torch. Think of the discussion as a chance to diagnose the challenge before choosing a solution.

Book a consultation with Torch.